Electronic storage mechanisms have enabled accumulation of massive amounts of data. For instance, data that previously required volumes of books for recordation can now be stored electronically without expense of printing paper and with a fraction of space needed for storage of paper. In one particular example, deeds and mortgages that were previously recorded in paper volumes can now be stored electronically. Moreover, advances in sensors and other electronic mechanisms now allow massive amounts of data to be collected and stored. For instance, Global Positioning Systems (GPS) can determine location of an individual or entity by way of satellites and GPS receivers, and electronic storage devices connected thereto can then be employed to retain locations associated with such systems. Various other sensors and data collection devices can also be utilized for obtainment and storage of data.
Collected data relating to particular contexts and/or applications can be employed in connection with data trending and analysis, and predictions can be made as a function of received and analyzed data. Such prediction is, in fact, human nature, and individuals frequently generate such predictions. For instance, a person traveling between a place of employment and a place of residence can determine that during certain times of day within weekdays, traffic conditions are subject to high levels of congestion. Thus, prior to leaving a place of work, an individual can predict when and where they will most likely be slowed in traffic, and can further predict how long they will be subject to congestion. The individual's predictions can further be a function of other variables, such as whether a day is a holiday, events that are geographically proximate, weather, and the like. Therefore, when an individual has access to contextual information and has access to (e.g., by way of memory) historical data, the individual can generate predictions.
Predictive models utilized on computer systems can often produce more accurate predictive results than a human, as computer systems may have access to a substantial amount of data. For instance, a computer application can have access to data that represents traffic patterns over twenty years, whereas an individual may have experienced traffic patterns for less than a year. These predictive models can be quite effective when generating predictions associated with common occurrences. Computer-implemented predictive models reliant upon substantial amounts of contextual data, however, can be associated with various deficiencies and/or problems with timely receiving data. For instance, a predictive model can be tasked to predict events and/or circumstances that will occur in approximately thirty minutes. It may be the case, however, that data received by a predictive model from one or more sensors can be subject to delays that are near thirty minutes (e.g., caused by delays in getting data from a sensor to various amplifying mechanisms or holding stations, to a server that retains a predictive model, . . . ). Furthermore, there may be uncertain delays in getting predictive data from the model (which can reside in a server) to a device associated with a user (e.g., a wristwatch, a personal digital assistant, a cellular phone, . . . ). These delays can be associated with bandwidth issues, lack of processing power in a server, physical impediments between a user and a communicating base station, etc. Thus, given the uncertainty in delay of data, a user's experience can be negatively affected because outdated information is provided to such user and/or data not as relevant to the user as disparate data is provided to the user.